Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Background: Maintenance of stable blood pressure (BP) during cerebrovascular bypass surgery is crucial to prevent cerebral ischemia. We compared the effect of remimazolam anesthesia with that of propofol-induced and desflurane-maintained anesthesia on intraoperative hemodynamic stability and the need for vasoactive agents in patients undergoing cerebrovascular bypass surgery. Methods: Sixty-five patients were randomized into remimazolam (n = 31, remimazolam-based intravenous anesthesia) and control groups (n = 34, propofol-induced and desflurane-maintained anesthesia). The primary outcome was the occurrence of intraoperative hypotension. The secondary outcomes included hypotension duration, lowest mean BP (MBP), generalized average real variability (ARV) of MBP, and consumption of phenylephrine, norepinephrine, or remifentanil. Results: Occurrence rate and duration of hypotension were significantly lower in the remimazolam group (38.7% vs. 73.5%, P = 0.005; 0 [0, 10] vs. 7.5 [1.25, 25] min, P = 0.008). Remimazolam also showed better outcomes for lowest MBP (78 [73, 84] vs. 69.5 [66.25, 75.8] mmHg, P < 0.001) and generalized ARV of MBP (1.42 ± 0.49 vs. 1.66 ± 0.52 mmHg/min, P = 0.036). The remimazolam group required less phenylephrine (20 [0, 65] vs. 100 [60, 130] μg, P < 0.001), less norepinephrine (162 [0, 365.5] vs. 1335 [998.5, 1637.5] μg, P < 0.001), and more remifentanil (1750 [1454.5, 2184.5] vs. 531 [431, 746.5] μg, P < 0.001) than the control group. Conclusions Remimazolam anesthesia may provide better hemodynamic stability during cerebrovascular bypass surgery than propofol-induced and desflurane-maintained anesthesia.

Purpose: Cancer poses a significant global health challenge, demanding precise genomic testing for individualized treatment strategies. Targeted-panel sequencing (TPS) has improved personalized oncology but often lacks comprehensive coverage of crucial cancer alterations. Whole-genome sequencing (WGS) addresses this gap, offering extensive genomic testing. This study demonstrates the medical potential of WGS. Materials and Methods: This study evaluates target-enhanced WGS (TE-WGS), a clinical-grade WGS method sequencing both cancer and matched normal tissues. Forty-nine patients with various solid cancer types underwent both TE-WGS and TruSight Oncology 500 (TSO500), one of the mainstream TPS approaches. Results: TE-WGS detected all variants reported by TSO500 (100%, 498/498). A high correlation in variant allele fractions was observed between TE-WGS and TSO500 (r=0.978). Notably, 223 variants (44.8%) within the common set were discerned exclusively by TE-WGS in peripheral blood, suggesting their germline origin. Conversely, the remaining subset of 275 variants (55.2%) were not detected in peripheral blood using the TE-WGS, signifying them as bona fide somatic variants. Further, TE-WGS provided accurate copy number profiles, fusion genes, microsatellite instability, and homologous recombination deficiency scores, which were essential for clinical decision-making. Conclusion TE-WGS is a comprehensive approach in personalized oncology, matching TSO500’s key biomarker detection capabilities. It uniquely identifies germline variants and genomic instability markers, offering additional clinical actions. Its adaptability and cost-effectiveness underscore its clinical utility, making TE-WGS a valuable tool in personalized cancer treatment.

Purpose: We assessed human papillomavirus (HPV) genotype-based risk stratification and the efficacy of cytology testing for cervical cancer screening in patients with atypical squamous cells of undetermined significance (ASCUS)/low-grade squamous intraepithelial lesion (LSIL). Materials and Methods: Between 2010 and 2021, we monitored 1,273 HPV-positive women with ASCUS/LSIL every 6 months for up to 60 months. HPV infections were categorized as persistent (HPV positivity consistently observed post-enrollment), negative (HPV negativity consistently observed post-enrollment), or non-persistent (neither consistently positive nor negative). HPV genotypes were grouped into high-risk (Hr) groups 1 (types 16, 18, 31, 33, 45, 52, and 58) and 2 (types 35, 39, 51, 56, 59, 66, and 68) and a low-risk group. Hr1 was subdivided into types (a) 16 and 18; (b) 31, 33, and 45; and (c) 52 and 58. Cox regression and machine learning (ML) algorithms were used to analyze progression rates. Results: Among 1,273 participants, 17.6% with persistent HPV infections experienced disease progression versus no progression in the HPV-negative group (p < 0.001). Cox analysis revealed the highest hazard ratios (HRs) for Hr1-a (11.6, p < 0.001), followed by Hr1-b (9.26, p < 0.001) and Hr1-c (7.21, p < 0.001). HRs peaked at 12-24 months, with Hr1-a maintaining significance at 24-36 months (10.7, p=0.034). ML analysis identified the final cytology change pattern as the most significant factor, with 14-15 months the optimal time for detecting progression from the first examination. Conclusion In ASCUS/LSIL cases, follow-up strategies should be based on HPV risk types. Annual follow-up was the most effective monitoring for detecting progression/regression.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Background: Maintenance of stable blood pressure (BP) during cerebrovascular bypass surgery is crucial to prevent cerebral ischemia. We compared the effect of remimazolam anesthesia with that of propofol-induced and desflurane-maintained anesthesia on intraoperative hemodynamic stability and the need for vasoactive agents in patients undergoing cerebrovascular bypass surgery. Methods: Sixty-five patients were randomized into remimazolam (n = 31, remimazolam-based intravenous anesthesia) and control groups (n = 34, propofol-induced and desflurane-maintained anesthesia). The primary outcome was the occurrence of intraoperative hypotension. The secondary outcomes included hypotension duration, lowest mean BP (MBP), generalized average real variability (ARV) of MBP, and consumption of phenylephrine, norepinephrine, or remifentanil. Results: Occurrence rate and duration of hypotension were significantly lower in the remimazolam group (38.7% vs. 73.5%, P = 0.005; 0 [0, 10] vs. 7.5 [1.25, 25] min, P = 0.008). Remimazolam also showed better outcomes for lowest MBP (78 [73, 84] vs. 69.5 [66.25, 75.8] mmHg, P < 0.001) and generalized ARV of MBP (1.42 ± 0.49 vs. 1.66 ± 0.52 mmHg/min, P = 0.036). The remimazolam group required less phenylephrine (20 [0, 65] vs. 100 [60, 130] μg, P < 0.001), less norepinephrine (162 [0, 365.5] vs. 1335 [998.5, 1637.5] μg, P < 0.001), and more remifentanil (1750 [1454.5, 2184.5] vs. 531 [431, 746.5] μg, P < 0.001) than the control group. Conclusions Remimazolam anesthesia may provide better hemodynamic stability during cerebrovascular bypass surgery than propofol-induced and desflurane-maintained anesthesia.

Background: and Purpose This study evaluated the diagnostic utility of an anti-signal-recognition particle 54 (anti-SRP54) antibody-based enzyme-linked immunosorbent assay (ELISA) as well as the clinical, serological, and pathological characteristics of patients with SRP immune-mediated necrotizing myopathy (IMNM). Methods: We evaluated 87 patients with idiopathic inflammatory myopathy and 107 healthy participants between January 2002 and December 2023. The sensitivity and specificity of the ELISA for anti-SRP54 antibodies were assessed, and the clinical profiles of patients with antiSRP54 antibodies were determined. Results: The ELISA for anti-SRP54 antibodies had a sensitivity and specificity of 88% and 99%, respectively, along with a test–retest reliability of 0.92 (p<0.001). The 32 patients diagnosed with anti-SRP IMNM using a line-blot immunoassay included 28 (88%) who tested positive for anti-SRP54 antibodies using the ELISA, comprising 12 (43%) males and 16 (57%) females whose median ages at symptom onset and diagnosis were 43.0 years and 43.5 years, respectively. Symptoms included proximal muscle weakness in all 28 (100%) patients, neck weakness in 9 (32%), myalgia in 15 (54%), dysphagia in 5 (18%), dyspnea in 4 (14%), dysarthria in 2 (7%), interstitial lung disease in 2 (7%), and myocarditis in 2 (7%). The median serum creatine kinase (CK) level was 7,261 U/L (interquartile range: 5,086–10,007 U/L), and the median anti-SRP54 antibody level was 2.0 U/mL (interquartile range: 1.0–5.6 U/mL). The serum CK level was significantly higher in patients with coexisting anti-Ro-52 antibodies. Conclusions This study has confirmed the reliability of the ELISA for anti-SRP54 antibodies and provided insights into the clinical, serological, and pathological characteristics of South Korean patients with anti-SRP IMNM.

Purpose: Cancer poses a significant global health challenge, demanding precise genomic testing for individualized treatment strategies. Targeted-panel sequencing (TPS) has improved personalized oncology but often lacks comprehensive coverage of crucial cancer alterations. Whole-genome sequencing (WGS) addresses this gap, offering extensive genomic testing. This study demonstrates the medical potential of WGS. Materials and Methods: This study evaluates target-enhanced WGS (TE-WGS), a clinical-grade WGS method sequencing both cancer and matched normal tissues. Forty-nine patients with various solid cancer types underwent both TE-WGS and TruSight Oncology 500 (TSO500), one of the mainstream TPS approaches. Results: TE-WGS detected all variants reported by TSO500 (100%, 498/498). A high correlation in variant allele fractions was observed between TE-WGS and TSO500 (r=0.978). Notably, 223 variants (44.8%) within the common set were discerned exclusively by TE-WGS in peripheral blood, suggesting their germline origin. Conversely, the remaining subset of 275 variants (55.2%) were not detected in peripheral blood using the TE-WGS, signifying them as bona fide somatic variants. Further, TE-WGS provided accurate copy number profiles, fusion genes, microsatellite instability, and homologous recombination deficiency scores, which were essential for clinical decision-making. Conclusion TE-WGS is a comprehensive approach in personalized oncology, matching TSO500’s key biomarker detection capabilities. It uniquely identifies germline variants and genomic instability markers, offering additional clinical actions. Its adaptability and cost-effectiveness underscore its clinical utility, making TE-WGS a valuable tool in personalized cancer treatment.

Purpose: We assessed human papillomavirus (HPV) genotype-based risk stratification and the efficacy of cytology testing for cervical cancer screening in patients with atypical squamous cells of undetermined significance (ASCUS)/low-grade squamous intraepithelial lesion (LSIL). Materials and Methods: Between 2010 and 2021, we monitored 1,273 HPV-positive women with ASCUS/LSIL every 6 months for up to 60 months. HPV infections were categorized as persistent (HPV positivity consistently observed post-enrollment), negative (HPV negativity consistently observed post-enrollment), or non-persistent (neither consistently positive nor negative). HPV genotypes were grouped into high-risk (Hr) groups 1 (types 16, 18, 31, 33, 45, 52, and 58) and 2 (types 35, 39, 51, 56, 59, 66, and 68) and a low-risk group. Hr1 was subdivided into types (a) 16 and 18; (b) 31, 33, and 45; and (c) 52 and 58. Cox regression and machine learning (ML) algorithms were used to analyze progression rates. Results: Among 1,273 participants, 17.6% with persistent HPV infections experienced disease progression versus no progression in the HPV-negative group (p < 0.001). Cox analysis revealed the highest hazard ratios (HRs) for Hr1-a (11.6, p < 0.001), followed by Hr1-b (9.26, p < 0.001) and Hr1-c (7.21, p < 0.001). HRs peaked at 12-24 months, with Hr1-a maintaining significance at 24-36 months (10.7, p=0.034). ML analysis identified the final cytology change pattern as the most significant factor, with 14-15 months the optimal time for detecting progression from the first examination. Conclusion In ASCUS/LSIL cases, follow-up strategies should be based on HPV risk types. Annual follow-up was the most effective monitoring for detecting progression/regression.